{"id":"W3119931323","doi":"10.1007/s42979-020-00403-9","title":"RGAN: Rényi Generative Adversarial Network","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Discriminator; Generative grammar; Adversarial system; Function (biology); Stability (learning theory); Computer science; Generative adversarial network; Artificial intelligence; Machine learning; Deep learning; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00126085,0.001225805,0.0007882531,0.0008393708,0.0003557534,0.0008207854,0.001806851,0.001892855,0.009530345],"category_scores_gemma":[0.003846707,0.0005363232,0.0007164768,0.0006370388,0.0008942875,0.001149972,0.002027312,0.003090688,0.004416204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000738252,"about_ca_system_score_gemma":0.0008000638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002745451,"about_ca_topic_score_gemma":0.004580073,"domain_scores_codex":[0.9994903,0.0001969805,0.00001565287,0.0001165108,0.0001397729,0.00004089981],"domain_scores_gemma":[0.999002,0.0005475454,0.00006317822,0.0002165755,0.0001244482,0.00004616059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001343904,0.00006801729,0.0003343559,0.0001211344,0.00009488167,0.0001258973,0.00003908069,0.6882813,0.00426553,0.07448904,0.02519087,0.2068556],"study_design_scores_gemma":[0.000006851893,0.00001064288,0.00004430297,0.00000973753,0.000006652677,0.00003268737,0.000002553039,0.9727827,0.001434998,0.02131997,0.004340872,0.000007973716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001491656,0.0002830769,0.9913471,0.0002729385,0.0001372432,0.00004160602,0.0002717849,0.003097114,0.00305748],"genre_scores_gemma":[0.2339579,0.0008818273,0.7251926,0.001071136,0.0002923744,0.0005124508,0.001864628,0.002284911,0.03394215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009530345,"threshold_uncertainty_score":0.03188217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01108056440714487,"score_gpt":0.2231220541459432,"score_spread":0.2120414897387983,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}